Enhanced Polarization Weighting to Enable Scalability in Polar Code Bit Distribution

ABSTRACT

Methods and devices are described for determining reliabilities of bit positions in a bit sequence for information bit allocation using polar codes. The reliabilities are calculated using a weighted summation over a binary expansion of each bit position, wherein the summation is weighted by an exponential factor that is selected based at least in part on the coding rate of the polar code. Information bits and frozen bits are allocated to the bit positions based on the determined reliabilities, and data is polar encoded as the information bits. The polar encoded data is then transmitted to a remote device.

PRIORITY CLAIM

This application is a continuation of U.S. application Ser. No.15/972,752, titled “Enhanced Polarization Weighting to EnableScalability in Polar Code Bit Distribution” and filed on May 7, 2018,which claims benefit of priority to U.S. Provisional Application No.62/503,172 titled “Implementation Considerations for Polar CodeConstruction” and filed on May 8, 2017, which is hereby incorporated byreference in its entirety as if fully and completely set forth herein.

The claims in the instant application are different than those of theparent application and/or other related applications. The Applicanttherefore rescinds any disclaimer of claim scope made in the parentapplication and/or any predecessor application in relation to theinstant application. Any such previous disclaimer and the citedreferences that it was made to avoid, may need to be revisited. Further,any disclaimer made in the instant application should not be read intoor against the parent application and/or other related applications.

FIELD OF THE INVENTION

The field of the invention generally relates to polar code construction.

DESCRIPTION OF THE RELATED ART

Polar codes are used in a wide variety of technological applications.When constructing a polar code, frozen bits and information bits areallocated to specific bit locations within the polar code. Existingalgorithms for polar code construction may be inefficientcomputationally, or may otherwise introduce undesirable features intothe polar code. Accordingly, improvements in the field are desired.

SUMMARY OF THE EMBODIMENTS

Various embodiments are described of systems and methods for determiningreliabilities of bit positions in construction of a polar code. Forexample, some embodiments may relate to a user equipment (UE) or a basestation (BS) that comprises at least one radio, a memory, and one ormore processing elements, and which is configured to perform a subset orall of the operations described herein.

In some embodiments, a UE constructs and transmits polar encoded data.The data to be polar encoded may be stored, and a coding rate for polarencoding the data may be determined. A reliability associated with eachbit position in a bit sequence may be determined by calculating asequence of weighted summations corresponding to each of the bitpositions. The reliability of each bit position may be determined fromits respective weighted summation, wherein the weighted summationcorresponding to each respective bit position is a weighted summationover a binary expansion of the respective bit position. The weightedsummation may be weighted by a first multiplicative factor, and thefirst multiplicative factor may be selected based at least in part onthe coding rate.

The bit positions may be rank ordered based on their respectivereliabilities, and the data may be allocated as information bits in themost reliable bit positions of a polar code based on the rank ordering.The remaining portion of bit positions may be allocated as frozen bitsof the polar code. The information bits and the frozen bits may be polarencoded, and the UE may then transmit the polar encoded information bitsand frozen bits.

This Summary is intended to provide a brief overview of some of thesubject matter described in this document. Accordingly, it will beappreciated that the above-described features are merely examples andshould not be construed to narrow the scope or spirit of the subjectmatter described herein in any way. Other features, aspects, andadvantages of the subject matter described herein will become apparentfrom the following Detailed Description, Figures, and Claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained when thefollowing detailed description of the preferred embodiment is consideredin conjunction with the following drawings, in which:

FIG. 1 is a diagram illustrating a wireless communication environment,according to some embodiments;

FIG. 2 is a diagram illustrating a wireless communication environmentwith base station coverage overlap, according to some embodiments;

FIG. 3 is a block diagram illustrating an exemplary base station,according to some embodiments;

FIG. 4 is a block diagram illustrating an exemplary UE, according tosome embodiments;

FIG. 5 illustrates an example of channel polarization, where n=11;

FIG. 6 illustrates an example polar encoder, where n=3;

FIG. 7 is a flow diagram illustrating an exemplary method for atransmitter to encode a message with polar codes using enhancedpolarization weighting (PW) code construction, according to someembodiments;

FIG. 8 is a plot of PW ordered sequence latency as a function of clockspeed for a variety of values of code sizes, according to someembodiments;

FIG. 9 is a graph of the power of the signal and error using PW as afunction of total bit number, according to some embodiments;

FIG. 10 illustrates a typical mutual information (MI) recursion methodemployed according to FRActally eNhanced Kernel (FRANK) polar codeconstruction, according to some embodiments;

FIG. 11 is a plot of recursion latency as a function of clock speedusing FRANK for different values of N, according to some embodiments;

FIG. 12 is a graph of a 4-bit nested β-expansion, according to someembodiments;

FIG. 13 is a graph of how different terms in a β-expansion vary as afunction of m, according to some embodiments;

FIG. 14 is a graph of normalized polarization weight as a function ofsynthetic channel index, according to some embodiments;

FIG. 15 is a schematic diagram illustrating a method for using FRANK todistribute information bits, according to some embodiments;

FIG. 16 is a scatter plot of bit position as a function of bit indexusing enhanced PW according to a variety of values of m, according tosome embodiments;

FIGS. 17a and 17b are scatter plots of assigned bit position as afunction of synthetic channel index for two values of m, according tosome embodiments;

FIG. 18 illustrates four scatter plots comparing the actual assigned bitpositions from the computed channel reliabilities to the resultsobtained using enhanced PW with various values of m, for four differentcode rates; and

FIG. 19 is a scatterplot illustrating average error in assigned channelreliability obtained using enhanced PW relative to the computed channelreliabilities as a function of code rate; according to some embodiments.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and are herein described in detail. It should beunderstood, however, that the drawings and detailed description theretoare not intended to limit the invention to the particular formdisclosed, but on the contrary, the intention is to cover allmodifications, equivalents and alternatives falling within the spiritand scope of the present invention as defined by the appended claims.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION Incorporation byReference

The following references are hereby incorporated by reference in theirentirety as though fully and completely set forth herein:

-   Reference 1. R1-1701702, ““Construction schemes for polar codes”,    Huawei, HiSilicon, TSG RAN WG1#88, February 2017.-   Reference 2. R1-1706130, “FRANK polar construction: nested extension    design of polar codes based on mutual information”, Qualcomm Inc.,    TSG RAN WG1#88bis, April 2017.-   Reference 3. R1-1613006, “A Dynamically Configurable Multi-mode NR    Decoder Implementation”, Coherent Logix Inc., 3GPP TSG RAN WG1    Meeting #87, November 2016.-   Reference 4. “β-expansion: A Theoretical Framework for Fast and    Recursive Construction of Polar Codes”, Cornell University Library,    Apr. 19, 2017.

Terms

The following is a glossary of terms used in the present application:

Memory Medium—Any of various types of memory devices or storage devices.The term “memory medium” is intended to include an installation medium,e.g., a CD-ROM, floppy disks, or tape device; a computer system memoryor random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, RambusRAM, etc.; or a non-volatile memory such as a magnetic media, e.g., ahard drive, optical storage, or ROM, EPROM, FLASH, etc. The memorymedium may comprise other types of memory as well, or combinationsthereof. In addition, the memory medium may be located in a firstcomputer in which the programs are executed, and/or may be located in asecond different computer which connects to the first computer over anetwork, such as the Internet. In the latter instance, the secondcomputer may provide program instructions to the first computer forexecution. The term “memory medium” may include two or more memorymediums which may reside in different locations, e.g., in differentcomputers that are connected over a network.

Carrier Medium—a memory medium as described above, as well as a physicaltransmission medium, such as a bus, network, and/or other physicaltransmission medium that conveys signals such as electrical or opticalsignals.

Programmable Hardware Element—includes various hardware devicescomprising multiple programmable function blocks connected via aprogrammable or hardwired interconnect. Examples include FPGAs (FieldProgrammable Gate Arrays), PLDs (Programmable Logic Devices), FPOAs(Field Programmable Object Arrays), and CPLDs (Complex PLDs). Theprogrammable function blocks may range from fine grained (combinatoriallogic or look up tables) to coarse grained (arithmetic logic units orprocessor cores). A programmable hardware element may also be referredto as “reconfigurable logic”.

Application Specific Integrated Circuit (ASIC)—this term is intended tohave the full breadth of its ordinary meaning. The term ASIC is intendedto include an integrated circuit customized for a particularapplication, rather than a general purpose programmable device, althoughan ASIC may contain programmable processor cores as building blocks.Cell phone chips, MP3 player chips, and many other single-function ICsare examples of ASICs. An ASIC is usually described in a hardwaredescription language such as Verilog or VHDL.

Program—the term “program” is intended to have the full breadth of itsordinary meaning. The term “program” includes 1) a software programwhich may be stored in a memory and is executable by a processor or 2) ahardware configuration program useable for configuring a programmablehardware element or ASIC.

Software Program—the term “software program” is intended to have thefull breadth of its ordinary meaning, and includes any type of programinstructions, code, script and/or data, or combinations thereof, thatmay be stored in a memory medium and executed by a processor. Exemplarysoftware programs include programs written in text-based programminglanguages, e.g., imperative or procedural languages, such as C, C++,PASCAL, FORTRAN, COBOL, JAVA, assembly language, etc.; graphicalprograms (programs written in graphical programming languages); assemblylanguage programs; programs that have been compiled to machine language;scripts; and other types of executable software. A software program maycomprise two or more software programs that interoperate in some manner.

Hardware Configuration Program—a program, e.g., a netlist or bit file,that can be used to program or configure a programmable hardware elementor ASIC.

Computer System—any of various types of computing or processing systems,including a personal computer system (PC), mainframe computer system,workstation, network appliance, Internet appliance, personal digitalassistant (PDA), grid computing system, or other device or combinationsof devices. In general, the term “computer system” can be broadlydefined to encompass any device (or combination of devices) having atleast one processor that executes instructions from a memory medium.

Automatically—refers to an action or operation performed by a computersystem (e.g., software executed by the computer system) or device (e.g.,circuitry, programmable hardware elements, ASICs, etc.), without userinput directly specifying or performing the action or operation. Thusthe term “automatically” is in contrast to an operation being manuallyperformed or specified by the user, where the user provides input todirectly perform the operation. An automatic procedure may be initiatedby input provided by the user, but the subsequent actions that areperformed “automatically” are not specified by the user, i.e., are notperformed “manually”, where the user specifies each action to perform.For example, a user filling out an electronic form by selecting eachfield and providing input specifying information (e.g., by typinginformation, selecting check boxes, radio selections, etc.) is fillingout the form manually, even though the computer system must update theform in response to the user actions. The form may be automaticallyfilled out by the computer system where the computer system (e.g.,software executing on the computer system) analyzes the fields of theform and fills in the form without any user input specifying the answersto the fields. As indicated above, the user may invoke the automaticfilling of the form, but is not involved in the actual filling of theform (e.g., the user is not manually specifying answers to fields butrather they are being automatically completed). The presentspecification provides various examples of operations beingautomatically performed in response to actions the user has taken.

Definitions of Acronyms

3GPP—Third Generation Partnership Program

5G—Fifth Generation (3GPP) cellular standard

BMS—Binary Memoryless Symmetric

FEC—Forward Error Correction

FRANK—FRActally eNhanced Kernel

PW—Polarization Weighting

RAN—Radio Access Network

TSG—Technical Standards Group

UPO—Universal Partial Order

WG—Working Group

DETAILED DESCRIPTION FIG. 1—Wireless Communication Environment

FIG. 1 illustrates an exemplary (and simplified) wireless environmentthat includes multiple communication systems. FIG. 1 shows an examplecommunication system involving a base station (BS) 102 communicatingwith a plurality of user equipment devices (UEs) 106A-C. The basestation 102 may be a cellular base station which performs cellularcommunications with a plurality of wireless communication devices.Alternatively, the base station 102 may be a wireless access point forperforming Wi-Fi communications, such as according to the 802.11standard or related standards. The UEs 106 may be any of various devicessuch as a smart phone, tablet device, computer system, etc. One or bothof the base station 102 and the wireless communication device 106 mayinclude polar encoding logic as described herein.

In the illustrated embodiment, different UEs and the base station areconfigured to communicate via a broadcast network and/or apacket-switched cellular network. It is noted that the system of FIG. 1is merely one example of possible systems, and embodiments may beimplemented in any of various systems, as desired.

Cellular base station 102 may be a base transceiver station (BTS) orcell site, and may include hardware that enables wireless communicationwith the UEs 106A-C. The base station 102 may also be configured tocommunicate with a core network. The core network may be coupled to oneor more external networks, which may include the Internet, a PublicSwitched Telephone Network (PSTN), and/or any other network. Thus, thebase station 102 may facilitate communication between the UE devices106A-C and a network.

Base station 102 and other base stations operating according to the sameor different radio access technologies (RATs) or cellular communicationstandards may be provided as a network of cells, which may providecontinuous or nearly continuous overlapping service to UEs 106A-C andsimilar devices over a wide geographic area via one or more RATs.

The base station 102 may be configured to broadcast communications tothe UEs 106A-C. The term “broadcast” herein may refer to one-to-manytransmissions that are transmitted for receiving devices in a broadcastarea rather than being addressed to a particular device. Further,broadcast transmissions are typically unidirectional (from transmitterto receiver). In some situations, control signaling (e.g., ratingsinformation) may be passed back to a broadcast transmitter from thereceivers, but the content data is transmitted in only one direction. Incontrast, cellular communication is typically bi-directional. “Cellular”communications also may involve handoff between cells. For example, whenUE 106A (and/or UEs 106B-C) moves out of the cell served by cellularbase station 102, it may be handed over to another cellular base station(and the handover may be handled by the network, including operationsperformed by base station 102 and the other cellular base station). Incontrast, when a user moves from the range covered by a first broadcastbase station to the range covered by a second broadcast base station, itmay switch to receiving content from the second broadcast base station,but the base stations do not need to facilitate handover (e.g., theysimply continue broadcasting and do not care which base station aparticular UE is using).

Traditionally, broadcast transmissions are performed using differentfrequency resources than cellular transmissions. In some embodiments,however, frequency resources are shared between these different types oftransmissions. For example, in some embodiments, a broadcast basestation is configured to relinquish one or more frequency bands duringscheduled time intervals for use by a cellular base station forpacket-switched communications.

In some embodiments, control signaling transmitted by a broadcast orcellular base station may allow end user devices to maintain fullsignaling connectivity (which may eliminate network churn), extendbattery life (e.g., by determining when to remain in a low power modewhen a base station is not transmitting), and/or actively managecoverage detection (e.g., rather than perceiving spectrum sharingperiods as spotty coverage or a temporary network outage).

The base station 102 and the UEs 106A, 106B, and 106C may be configuredto communicate over the transmission medium using any of various RATs(also referred to as wireless communication technologies ortelecommunication standards), such as LTE, 5G New Radio (NR), NextGeneration Broadcast Platform (NGBP), W-CDMA, TD-SCDMA, and GSM, amongpossible others such as UMTS, LTE-A, CDMA2000 (e.g., 1×RTT, 1×EV-DO,HRPD, eHRPD), Advanced Television Systems Committee (ATSC) standards,Digital Video Broadcasting (DVB), etc. In general, any of thetransmissions between the base station 102 and the UEs 106A, 106B, and106C may utilize the methods of enhanced PW for polar code constructionto polar encode the transmitted data, according to embodiments describedherein.

Broadcast and cellular networks are discussed herein to facilitateillustration, but these technologies are not intended to limit the scopeof the present disclosure and the disclosed spectrum sharing techniquesmay be used between any of various types of wireless networks, in otherembodiments.

FIG. 2—Wireless Communication Environment with Multiple Base Stations

FIG. 2 illustrates an exemplary wireless communication system thatincludes base stations 102A and 102B which communicate over atransmission medium with one or more user equipment (UE) devices,represented as UEs 106A-106C. The communication environment in FIG. 2may function similarly to that described in FIG. 1, above. However, FIG.2 illustrates that the center UE 106B may operate within range of bothof the base stations 102A and 102B. In these embodiments, UE 106B maymistakenly receive a communication from base station 102B when it wasintending to receive communications from base station 102A.

FIG. 3—Base Station

FIG. 3 illustrates an exemplary block diagram of a base station 102. Insome embodiments, base station 102 may be a broadcast base station suchas base station 102A of FIG. 2 and/or a cellular base station such asbase station 102B of FIG. 2. It is noted that the base station of FIG. 3is merely one example of a possible base station. As shown, the basestation 102 may include processor(s) 304 which may execute programinstructions for the base station 102. The processor(s) 304 may also becoupled to memory management unit (MMU) 340, which may be configured toreceive addresses from the processor(s) 304 and translate thoseaddresses to locations in memory (e.g., memory 360 and read-only memory(ROM) 350) or to other circuits or devices.

The base station 102 may include at least one network port 370. Thenetwork port 370 may be configured to couple to a telephone network andprovide a plurality of devices, such as UE devices 106, access to thetelephone network as described above. In some embodiments, the networkport 370 (or an additional network port) may be coupled to a televisionnetwork and configured to receive content for broadcasting. The networkport 370 (or an additional network port) may also or alternatively beconfigured to couple to a cellular network, e.g., a core network of acellular service provider. The core network may provide mobility relatedservices and/or other services to a plurality of devices, such as UEdevices 106. In some cases, the network port 370 may couple to atelephone network via the core network, and/or the core network mayprovide a telephone network (e.g., among other UE devices 106 servicedby the cellular service provider).

The base station 102 may include at least one antenna 334. The at leastone antenna 334 may be configured to operate as a wireless transceiverand may be further configured to communicate with UE devices 106 viaradio 330. The antenna 334 communicates with the radio 330 viacommunication chain 332 in the illustrated embodiment. Communicationchain 332 may be a receive chain, a transmit chain or both. The radio330 may be configured to communicate via various RATs.

The processor(s) 304 of the base station 102 may be configured toimplement part or all of the methods described herein, e.g., byexecuting program instructions stored on a memory medium (e.g., anon-transitory computer-readable memory medium). Alternatively, theprocessor 304 may be configured as a programmable hardware element, suchas an FPGA (Field Programmable Gate Array), or as an ASIC (ApplicationSpecific Integrated Circuit), or a combination thereof. In someembodiments, the processor, MMU, and memory may be a distributedmultiprocessor system. For example, the processor system may comprise aplurality of interspersed processors and memories, where processingelements (also called functional units) are each connected to aplurality of memories, also referred to as data memory routers. Theprocessor system may be programmed to implement the methods describedherein.

In some embodiments, base station 102 is configured to perform bothbroadcast and bi-directional packet-switched communications. In theseembodiments, base station 102 may include multiple radios 330,communication chains 332, and/or antennas 334, for example. In otherembodiments, the disclosed spectrum sharing techniques may be performedby different base stations configured to perform only broadcasttransmissions or only packet-switched communications.

FIG. 4—User Equipment (UE)

FIG. 4 illustrates an example simplified block diagram of a UE 106. TheUE 106 may be any of various devices as defined above. UE device 106 mayinclude a housing which may be constructed from any of variousmaterials.

As shown, the UE 106 may include a system on chip (SOC) 400, which mayinclude portions for various purposes. The SOC 400 may be coupled tovarious other circuits of the UE 106. For example, the UE 106 mayinclude various types of memory (e.g., including NAND flash 410), aconnector interface 420 (e.g., for coupling to a computer system, dock,charging station, etc.), the display 460, wireless communicationcircuitry 430 such as for LTE, 5G New Radio (NR), GSM, Bluetooth (BT),WLAN, and/or broadcast, etc. The UE 106 may further comprise one or moresmart cards that implement SIM (Subscriber Identity Module)functionality. The wireless communication circuitry 430 may couple toone or more antennas, such as antenna 435.

As shown, the SOC 400 may include processor(s) 402 which may executeprogram instructions for the UE 106 and display circuitry 404 which mayperform graphics processing and provide display signals to the display460. The processor(s) 402 may also be coupled to memory management unit(MMU) 440, which may be configured to receive addresses from theprocessor(s) 402 and translate those addresses to locations in memory(e.g., memory (e.g., read only memory (ROM) or another type of memory)406, NAND flash memory 410) and/or to other circuits or devices, such asthe display circuitry 404, wireless communication circuitry 430,connector I/F 420, and/or display 460. The MMU 440 may be configured toperform memory protection and page table translation or set up. In someembodiments, the MMU 440 may be included as a portion of theprocessor(s) 402. In some embodiments, the processor, MMU, and memorymay be a distributed multiprocessor system. For example, the processorsystem may comprise a plurality of interspersed processors and memories,where processing elements (also called functional units) are eachconnected to a plurality of memories, also referred to as data memoryrouters. The processor system may be programmed to implement the methodsdescribed herein.

In some embodiments (not shown), UE 106 is configured to receivewireless broadcasts, e.g., from broadcast base station 102A of FIG. 2.In these embodiments, UE 106 may include a broadcast radio receiver. Insome embodiments, UE 106 is configured to receive broadcast data andperform packet-switched cellular communications (e.g., LTE) at the sametime using different frequency bands and/or the same frequency resourcesduring different time slices. This may allow users to view TV broadcastswhile performing other tasks such as browsing the internet (e.g., in asplit-screen mode), using web applications, or listening to streamingaudio. In other embodiments, the disclosed techniques may be used insystems with devices that are configured as broadcast receivers or forcellular communications, but not both.

The processor(s) 402 of the UE device 106 may be configured to implementpart or all of the features described herein, e.g., by executing programinstructions stored on a memory medium (e.g., a non-transitorycomputer-readable memory medium). In some embodiments, the processor(s)402 may comprise a multiprocessor array of a plurality of parallelizedprocessing elements. For example, the processor(s) 402 may be designedin accordance with the HyperX architecture described in detail inReference 6, or another parallel processor architecture. In theseembodiments, separate ones of the parallelized processing elements maybe configured to perform decoding procedures on separate respective bitpaths of a successive cancellation list (SCL) decoding procedure, orthey may be configured to perform decoding procedures on separateencoded messages in parallel, for example. Alternatively (or inaddition), processor(s) 402 may be configured as a programmable hardwareelement, such as an FPGA (Field Programmable Gate Array), or as an ASIC(Application Specific Integrated Circuit). Alternatively (or inaddition) the processor(s) 402 of the UE device 106, in conjunction withone or more of the other components 400, 404, 406, 410, 420, 430, 435,440, 460 may be configured to implement part or all of the featuresdescribed herein.

UE 106 may have a display 460, which may be a touch screen thatincorporates capacitive touch electrodes. Display 460 may be based onany of various display technologies. The housing of the UE 106 maycontain or comprise openings for any of various elements, such asbuttons, speaker ports, and other elements (not shown), such asmicrophone, data port, and possibly various types of buttons, e.g.,volume buttons, ringer button, etc.

The UE 106 may support multiple radio access technologies (RATs). Forexample, UE 106 may be configured to communicate using any of variousRATs such as two or more of Global System for Mobile Communications(GSM), Universal Mobile Telecommunications System (UMTS), Code DivisionMultiple Access (CDMA) (e.g., CDMA2000 1×RTT or other CDMA radio accesstechnologies), Long Term Evolution (LTE), LTE Advanced (LTE-A), 5G NR,and/or other RATs. For example, the UE 106 may support at least tworadio access technologies such as LTE and GSM. Various different orother RATs may be supported as desired.

In some embodiments, UE 106 is also configured to receive broadcastradio transmissions which may convey audio and/or video content. Instill other embodiments, a UE 106 may be configured to receive broadcastradio transmissions and may not be configured to perform bi-directionalcommunications with a base station (e.g., UE 106 may be a media playbackdevice).

Polar Codes

Polar codes are being increasingly used in a variety of technologicalapplications. For example, it is anticipated that 5G NR communicationsbetween a UE (such as UE 106) and a base station (such as BS 102) mayemploy polar codes. Polar codes are a specific type of forward errorcorrection (FEC) codes.

Methods for constructing capacity achieving codes for the memorylessbinary symmetric channel are known in the art. FIG. 5 illustrates aphenomenon known as channel polarization leveraged through polar codes,wherein a polar code of length N=2048 bits is encoded through n=11stages of polar encoding. As described in FIG. 1, the resulting polarcodes leverage a phenomenon known as channel polarization whichtransforms a physical channel W into an arrangement of syntheticchannels W_(N) ^(i), the respective capacities of which, i.e. maximummutual information between channel input and output, tend toward 1(highly reliable) or 0 (highly unreliable). The corresponding bitprobabilities correspondingly approach 1 and 0.5, respectively, as thecode length, N=2^(n), increases with positive, non-zero integer valuesn.

Data may be transferred by placing information on bits on the mostreliable channels, and these bits may be referred to as informationbits. Bits placed on the least reliable channels may be set to a fixedvalue, e.g. 0 or another known value or set of values that is known toboth the transmitter and receiver, and these bits may be referred to asfrozen bits. Frozen bits and their mapping to the code matrix may beknown by both the transmitter and receiver. As a result, frozen bitpositions are set to their known values by a decoding algorithm at areceiver as part of the decoding process. Constructing a polar code ofblock length N may entail identifying an ordered sequence that reflectsthe synthetic channel reliabilities, assigning user information in themost reliable channel positions, and assigning the frozen bit pattern(not precluding the all-zeros dataset) in the least reliable channelpositions.

Polar codes form a class of linear block codes described by a generatormatrix, G. Polar codes of block lengths N may be generated according to:

G=F _(N)

(F ₂)^(⊗n)

Where F_(N) denotes the Kronecker product of

${F_{2} = \begin{pmatrix}1 & 0 \\1 & 1\end{pmatrix}},$

among other possibilities.

A polar code is defined by the location of k information bits and (N−k)frozen bits in a block of length, N. The code rate,

$R = \frac{k}{N}$

is expressed as the ratio of non-frozen bits to the block length. Thecode rate can be adjusted linearly by varying the number of non-frozenbits per block. Typically, the block length, N, is chosen to be a powerof two, such that N=2^(n), where n is a natural number.

In some embodiments, channel polarization splits a binary memorylesssymmetric (BMS) channel W:X→Y into a pair of synthetic channels W⁰:X→Y²and W¹:X×Y² where:

W ⁰(y ₁ ,y ₂ |x ₁)=½Σ_(x) ₂ _(∈X) W(y ₁ |x ₁ ⊕x ₂)W(y ₂ |x ₂)

W ¹(y ₁ ,y ₂ ,x ₁ |x ₂)=½W(y ₁ |x ₁ ⊕x ₂)W(y ₂ |x ₂)

Here ⊕ indicates an exclusive-OR (XOR) operation. This splitting may beapplied n times to obtain the N=2^(n) synthetic channels (e.g., seeReference labove).

The corresponding mutual information (MI) may be computed as follows:

I(W ¹)=2I(W)−I(W)²

I(W ⁰)=I(W)²

The set of synthetic channels, each with binary expansion (b₀, b₁, . . ., b_(n−1)), b_(i)∈{0, 1}, may be defined as:

W _(N) ^(i)=((W ^(b) ⁰ )^(b) ¹ . . . )^(b) ^(n−1) ,i=0, . . . ,N−1

If the mutual information I(W_(N) ^(i))>I(W_(N) ^(i)), then it may bedetermined that the synthetic channel W_(N) ^(i) is more reliable thanW_(N) ^(i), denoted as W_(N) ^(i)>W_(N) ^(i), where I(W)≡I(X;Y).

Mutual information, which may be defined as

${{I\left( {X;Y} \right)} = {\Sigma_{y \in Y}\Sigma_{x \in X}{p\left( {x,y} \right)}{\log \left( \frac{p\left( {x,y} \right)}{{p(x)}{p(y)}} \right)}}},$

provides a measure of the information a pair of random variables share,i.e. how much knowing one variable reduces the uncertainty of knowingthe other, where p(x,y) is the joint probability function of X and Y,and p(x) and p(y) are the marginal probability distribution functions ofX and Y, respectively. If X and Y are independent, p(x,y) p(x)p(y)yielding

${{\log \left( \frac{p\left( {x,y} \right)}{{p(x)}{p(y)}} \right)} = {{\log (1)} = 0}},$

i.e. the mutual information between random variables X and Y is zero.

FIG. 6—Exemplary Polar Encoder

FIG. 6 shows a sample polar code construction for block length N=2³. Theencoder begins with inputs, u_(i), which are encoded into outputs,x_(i). Information bits are shown in bold. The remaining inputs may beassigned frozen bit values, 0. At each stage, s, the encoder combinespairs of bits according to the encoding tree shown to the right.

Polar Code Construction

Polar code construction involves a permutation in bit position toreflect a rank ordering of the synthetic channel reliabilities. Toconstruct a code of length N, the information bit set is mapped to the Kmost reliable channel positions while the frozen bit set is placed inthe remaining N−K channel positions. The code rate is computed as R=K/N,i.e. the ratio of the number of information bits to the code blocklength.

The recursion needed to compute the exact synthetic channelreliabilities for a given block size N represents considerablecomputational load. As a result, a number of computationally lessexpensive methods have been proposed to arrive at ordering the bitpositions in a manner that approximates the exact channel reliabilities.In other words, to reduce computational load and time requirements,approximate methods of allocating information bits in a polar code havebeen developed.

Various methods are known in the art for polar code construction, suchas Polarization Weight (PW) (see Reference 1, above) and FRActallyeNhanced Kernel (FRANK) polar code construction (see Reference 2,above). While both PW and FRANK are able to allocate information bits ina polar code with reasonable accuracy, they both exhibit limitationswith respect to their accuracy (e.g., their accuracy relative to theexact synthetic channel reliabilities), latency of computation, andfinite precision effects.

Embodiments herein improve upon legacy PW and FRANK polar codeconstruction by implementing an enhanced PW polar code construction, toimprove computational efficiency and information bit allocation. Someembodiments herein present novel means for efficient computation of theordered sequence and assignment of bit positions as implemented in afinite precision Memory in Network Processor (MiNP) architecture such asthat described in Reference 3, above.

Enhanced Polarization Weighting (PW)

Embodiments herein describe an enhanced PW methodology for allocatinginformation bits in a polar code. Enhanced PW utilizes a technique knownas β-expansion, which provides a theoretical framework to enable fastdetermination of the ordered sequence used in polar code construction.Enhanced PW utilizes a generalized β-expansion, the procedure for whichcan be summarized as follows:

Take B_(i)≙b_(n−1)b_(n−2) . . . b₀ as the binary expansion of thesynthetic channel index, i=0, 1, . . . , N−1, where b_(j)∈{0,1}, j=[0,1, . . . , n−1],

Compute the polarization weights,

${w_{i} = {\sum\limits_{j = 0}^{n - 1}\; {b_{j} \cdot \beta_{j}}}},{{{where}\mspace{14mu} \beta_{j}} = 2^{j \cdot \frac{1}{m}}}$

where the integer value, m>0, determines the rate of

expansion,

${i.e.w_{i}} = {{b_{n - 1} \cdot 2^{\frac{n - 1}{m}}} + {b_{n - 2} \cdot 2^{\frac{n - 2}{m}}} + \cdots + {b_{0} \cdot 2^{\frac{0}{m}}}}$

Sort such that w_(Q) ₀ ≤W_(Q) ₁ ≤W_(Q) ₂ ≤ . . . ≤w_(Q) _(N−1) then savethe resulting indices as the ordered sequence, q₀ ^(N−1).

Traditional PW implementations use a fixed value of m=4 in theβ-expansion. Embodiments herein improve upon legacy PW implementationsby generalizing the β-expansion to other values of m. Advantageously,and as described in further detail below, strategically selecting mbased on the coding rate and/or the block size of the polar code mayimprove the accuracy of the resulting information block allocation.

Additionally, while the PW method as proposed for 3GPP 5G polar codeconstruction is based on β-expansion (See Reference 2), a competingframework, FRANK (FRActally eNhanced Kernel) (See Reference 3), claimsscalability with code rate (i.e. the number of information bits K for agiven block size N) as its principal advantage over the legacy PWmethod. Embodiments herein which extend the PW method to include avariable expansion factor, e.g. β=2^(1/m), may yield bit distributionsthat scale with code rate in a manner comparable to that exhibited withFRANK, but without some of the computational shortcomings associatedwith FRANK, as described in greater detail below. For example, β may betuned based on block size and code rate in a manner that improves theresulting match to the computed channel reliabilities.

FIG. 7—Enhanced PW Polar Code Construction

FIG. 7 is a flowchart diagram illustrating a method for transmittingpolar coded data, wherein the polar code has been constructed usingenhanced PW, according to some embodiments. The method shown in FIG. 7may be used in conjunction with any of the systems or devices shown inthe above Figures, among other devices. For example, the method shown inFIG. 7 may be used by a UE 106 in communication with another UE or abase station 102, or by a base station in communication with a UE oranother base station. More generally, the method may be employed by anywired or wireless polar encoded transmission from one device to another.In various embodiments, some of the method elements shown may beperformed concurrently, in a different order than shown, or may beomitted. Additional method elements may also be performed as desired. Asshown, this method may operate as follows.

At 702, data to be polar encoded may be stored. The data may be storedin a memory of a UE device, or another type of device configured totransmit polar coded data.

At 704, a coding rate for polar encoding the data may be determined. Thecoding rate K may be determined based on a variety of factors, includingsignal strength, channel conditions, transmission power, battery levelof the device, etc. For example, under good channel conditions a highercoding rate may be used to transmit data with a more efficientthroughput. In contrast, under poor channel conditions a lower codingrate may be used to ensure that data is only transmitted on veryreliable bits, to reduce the likelihood of transmission errors.

At 706, a reliability associated with each bit position in a bitsequence may be determined. The reliabilities may be determined bycalculating a sequence of weighted summations corresponding to each ofthe bit positions. The reliability of each bit position may bedetermined from its respective weighted summation, wherein the weightedsummation corresponding to each respective bit position is a weightedsummation over a binary expansion of the respective bit position. Forexample, the weighted summation may be performed according to enhancedPW utilizing a generalized β-expansion, as variously described above.

The summation may be weighted based on the determined coding rate forpolar encoding the data. For example, the weighted summation may beweighted by a first multiplicative factor, wherein the firstmultiplicative factor is selected based at least in part on the codingrate. In some embodiments, the multiplicative factor may be anexponential quantity (e.g., 2^(x) for some value of x, or anotherexponential quantity), and the exponential quantity may comprise a firstexponential factor, wherein the first exponential factor is theexponential power of each term in the binary expansion being summedover. For example, as described in greater detail above, the term in abinary expansion corresponding to 8=2³ may be associated with a firstexponential factor of 3. In some embodiments, the exponential quantitymay further comprise a second exponential factor, 1/m, wherein thesecond exponential factor comprises an adjustable parameter that istunable based at least in part on the coding rate.

As described in greater detail below, the exact channel reliabilities athigher code rates may be more closely approximated by enhanced PW iflarger values of m are used. For example, as illustrated in FIGS. 18-19below, for a low code rate of K=16 (top left of FIG. 18) the exactchannel reliability (“IDX” in FIG. 18) is more closely approximated bym=2 than by m=14. In contrast, at the higher code rates of K=64 andK=128 (bottom left and bottom right of FIG. 18) the exact channelreliability is more closely approximated by m=14 than m=2. Asillustrated in FIG. 16, larger values of m produce an information bitallocation with more information bits at earlier bit positions thansmaller values of m. Similarly, larger coding rates result in exactchannel reliabilities that promote information bit allocation withearlier bit positions. One limitation of traditional PW implementationsis that it is ineffective at allocating information bits to earlier bitlocations for higher code rates. By using a larger value of m for highercode rates, and a smaller value of m for smaller code rates, enhanced PWis able to more closely approximate the exact channel reliabilities.

In some embodiments, the second exponential factor is further selectedto spread a distribution of the information bits of the polar code toearlier bit positions relative to that obtained from selecting a firstexponential factor of ¼. For example, as illustrated in FIG. 16, largervalues of m produce an information bit allocation that is more widelyspread throughout the bit positions of the bit sequence than smallervalues of m. Because the information bit allocation corresponding to theexact channel reliabilities are more widely distributed throughout thebit sequence for larger coding rates, increasing the value of m, to belarger than m=4 may more closely approximate the information bitallocation corresponding to the exact channel reliabilities for largercoding rates.

In some embodiments, the summation may be weighted further based on ablock size of the polar code (e.g., the multiplicative factor may befurther selected based on the block size of the polar code). Forexample, as described in greater detail below in reference to Table 4,an error magnitude may be determined for various values of m for eachvalue of block size and coding rate employed by the polar code. A valueof m may be selected that minimizes the discrepancy between thedetermined reliabilities and the exact channel reliabilities.

In some embodiments, the multiplicative factor is further selected suchthat the polar code approximates a corresponding polar code, wherein thecorresponding polar code implements the determined coding rate and isconstructed using an alternative polar code construction methodology.For example, the polar code may be constructed according to an enhancedpolarization weighting methodology, and the alternative polar codeconstruction methodology may comprise a FRANK polar code constructionmethodology. As explained in greater detail below in reference to Table3, altering the value of m depending on the value of the coding rate Kmay result in identical information bit allocation in the K011[192,255]block for enhanced PW and FRANK polar code construction.

In some situations, FRANK may result in a more desirable information bitallocation (e.g., FRANK tends to allocate more information bits earlierin the polar code, leading to faster reception of information bits bythe receiver and resulting in a closer approximation to the exactchannel reliabilities). However, as explained in further detail below,FRANK may be subject to adverse finite precision effects, whereas finiteprecision effects are negligible with PW. Enhanced PW, which may be usedto approximate the information bit allocation of FRANK while avoidingthe finite precision effects of FRANK, may therefore offer substantialimprovement over existing methods for polar code construction.

Referring back to FIG. 7, at 708 the bit positions may be rank orderedbased on their respective reliabilities. For example, the bit positionsmay be rank ordered in ascending order of their respective determinedreliabilities.

At 710, the data may be allocated as information bits in the mostreliable bit positions of the polar code based on the rank ordering. Theremaining bit positions may be allocated as frozen bits of the polarcode.

At 712, the information bits and the frozen bits may be encoded. Forexample, the information bits and the frozen bits may be processedthrough a polar encoding algorithm to obtain polar encoded informationbits and frozen bits.

At 714, the polar encoded information bits and frozen bits may betransmitted. For example, a UE may wirelessly transmit the polar encodedinformation bits and frozen bits as a polar encoded message to anotherremote UE or to a base station.

In some embodiments, the method described in reference to steps 702-714may be repeated for second data to be polar encoded according to asecond coding rate. For example, second data to be polar encoded may bestored, and a second coding rate for polar encoding the second data maybe determined. If the second coding rate is different from the firstcoding rate, second reliabilities may be determined using weightedsummations, wherein the weighted summation is weighted by a secondmultiplicative factor that is selected based on the second coding rate.In other words, in subsequent rounds of data transmission, the UE mayadapt the reliability determination based on the coding rate used foreach particular data transmission. For example, the UE maycorrespondingly perform second rank ordering of the bit positions basedon their respective second reliabilities, encode the second data in themost reliable bit positions as information bits of a second polar codebased on the second rank ordering, allocate the remaining portion of bitpositions as frozen bits of the second polar code, and transmit theinformation bits and the frozen bits of the second polar code.

Detailed Analysis of FRANK, PW, and Enhanced PW

The following sections present a more detailed analysis of informationbit allocation results for each of FRANK, PW, and enhanced PWmethodologies. These sections provide additional detail and support foradvantages incurred by the described methods of FIG. 7.

Universal Partial Order (UPO)

A partial order of reliability measure exists for any binary symmetricchannel. This reliability measure may be insufficient to form a fullyordered sequence over N bit positions, each corresponding to an index ofthe synthetic channel, and is therefore deemed partial.

Addition:

Given a synthetic channel whose index C_(A) has a binary representation(b₃, b₂, 1, b₀), it is less reliable than the synthetic channel whosebinary index C_(B) has a binary representation (b₃, b₂, 0, b₀).Considering the occurrence of “1

0” in one or multiple bit positions:

2(0,1, 0)

3(0,1, 1),

9(1, 0,0, 1)

15(1, 1,1, 1).

Left-Swap:

Given a so-called left-swap such that “0 . . . 1

1 . . . 0” where the pattern can occur multiple times and the bitpositions need not be adjacent:

2(0,1, 0)

4(1,0, 0),

12(0, 1, 1, 0,0)

24(1, 1, 0, 0,0).

The above properties are used to assess relative reliabilities betweenpairs of channel indices (x, y), a subset of which cannot be determinedby Addition or Left-swap or their combination. These orders areconsequently unknown to UPO (hence the term partial order):

3(0,1,1) and 4(1,0,0),

7(0,1,1,1) and 12(1,1,0,0).

The UPO is referred to as “universal” because the approach holds for anybinary symmetric channel.

Polarization Weighting (PW)

PW polar code construction is a method based in Binary Expansion (BE),and aimed at addressing the reliability order issue. The procedure forderiving the ordered sequence and info/frozen bit positions using PWpolar code construction may be summarized as follows:

A signal-to-noise ratio (SNR)-independent reliability estimation isperformed by computing the reliability of each sub-channel (offlineoperation), and storing the ordered index sequence, Q₀ ^(N) ^(max) ⁻¹,for the polar code of maximum code length N_(max). The reliability orderof sub-channels is estimated through a weight sequence W₀ ^(N) ^(max)⁻¹, calculated as follows:

Assume i

b _(n−1) b _(n−2) . . . b ₀ with b _(j)∈{0,1},j=[0,1, . . . ,n−1], then,

W ^(i)=Σ_(j=0) ^(n−1) b _(j)*2^(j*1/4), where n=log₂(N).

Design Analysis for PW—Computational Load

The PW method of code construction involves two sets of computations:(i) accumulating the weighted sum of each x-domain index; (ii) rankordering the weighted sums to determine the u-domain mapping.

In computing the weighted sum in PW, the progression in β_(j) may becomputed offline using the formula: β_(j)=2^(j·1/4). The weighted sumcomputation amounts to a series of summations of the form W_(i)=Σ_(j=0)^(n−1)b_(i,j)·β_(j), i=0, . . . , N−1, requiring n·N real multiplyaccumulate operations for the entire weighted set, W₀ ^(N−1). Thiscalculation may be parallelized along dimensions, i or j, as needed toreduce latency.

In performing the rank ordering, the weighted sums may be rank orderedwith N·log₂N complexity. This complexity reduces to n·N, given thatn=log₂N. This computation cannot be easily parallelized. However, givenmodest block sizes, this computation may be performed online.

FIG. 8: Latency Analysis—PW Ordered Sequence Generation

FIG. 8 is a plot of PW ordered sequence latency as a function of clockspeed for a variety of values of code sizes, N. As illustrated, thelatency for the ordered sequence computation is less than 20 μs given aminimum of a 1 GHz clock and assuming no parallelism. With a minimum 500MHz clock, the latency remains below 50 μs for even the largest blocksize. Taking opportunities to parallelize the weighted sum calculationinto account, the latency may be further reduced below that illustratedin FIG. 8.

FIG. 9: Quantization Error Analysis—PW Ordered Sequence Generation

FIG. 9 is a graph of the power of the signal (|W|²) and error(|W−W_(q)|²) using PW as a function of total bit number. The dynamicrange for the weighted sum computation is bounded by zero for i=0 [000 .. . 0]₂ and Σβ_(j) for i=N−1 [111 . . . 1]₂. Given β_(j)=2^(j·1/4), themaximum value for the weighted sum can be shown to be W_(max)=19.8556.The relative mean-squared error is shown in FIG. 9 for a range of bitrepresentations assuming 5 bits to the left of the decimal point withthe remaining bits used to represent the fractional portion. Thequantization noise power is reduced below 75 dB for modest bit widths.The MiNP architecture being used for the evaluation illustrated in FIG.9 is based on 16-bit data. As illustrated, quantization error isnegligible for the PW method.

FRActally eNhanced Kernel (FRANK)

FRANK polar code construction was introduced to provide a theoreticallyjustifiable framework for scalable polar code sequence construction withlow description complexity to enable online construction. The procedurefor deriving the ordered sequence and info/frozen bit positions usingFRANK may be summarized as follows:

1) Construct a short reference sequence based on Nested mutualinformation (MI) density evolution (DE), or other schemes.

2) For each (N, K) code, recursively partition a long codeword intogroups of small length (the shortest length equals the length of theconstructed short reference sequence) and allocate a number ofinformation (info) bits Ki to each group based on the information-bitratio according to the MI ratio formula, and taking into accountpunctured bits and/or shortened bits.

3) Generate information bit locations of the group of length Nref basedon the short reference sequence and a number of information bits in thatgroup when the block of length Nref or shorter is reached.

FIG. 10: FRANK MI Recursion

FIG. 10 illustrates a typical mutual information (MI) recursion methodemployed according to FRANK polar code construction.

In the case where the code size is a power of 2, N=2^(m), theinformation bit distribution is based on a MI calculation where theinput MI to the recursion is set to capacity MI=R=K/N (that is, themaximum rate a channel code can support). Recursive calculation of therate allocation for each group, e.g. R0, R1, and subsequently, R00, R01,R10, R11, etc. is carried out as depicted in FIG. 10.

Design Analysis for FRANK—Computational Load

The computational load in FRANK is usually dominated by R² and 2R−R²computation pairs. M segments of Nref each require m=log₂M stages,incurring 2¹+2²+2³+ . . . computation pairs. Each pair requires onemultiply and one multiply-accumulate for a total of two operations perpair, per stage. The total number of operations is given by 2·Σ_(i=1)^(m)2^(m)=2^(m+2)−4. N=1024 and Nref=64, M=16 requires m=4 stages. Thetotal operation count is 26200. As illustrated in FIG. 11, which is aplot of recursion latency as a function of clock speed using FRANK fordifferent values of N, the corresponding latency is negligible for anyclock speed.

FRANK—Finite Precision Effects

The precision requirements for polar codes constructed using FRANK aredetermined by R−R^(M) terms which, for low code rates and long blocksizes, may cause an underflow in fixed-point representation resulting infewer assigned bit indices than required for the selected code rate.However, taking the coarse granularity associated with distributing Kinformation bits to M sub-blocks, the precision requirements may berelaxed. A heuristic approach suggests that 13 or 14 fixed-point bitsmay suffice for accurate computation.

The choice of information bit distribution among respective groups oflength Nref is established in Reference 2 as being instrumental indevising a code that is capacity achieving. Table 1, below, lists theK-distribution for FRANK alongside that for enhanced PW. Note that theenhanced PW K-distribution may be tuned to approximate the sparsity ofinformation bits in the upper part of the FRANK code sequences. Thisconsideration has been taken into account to promote an equitableside-by-side comparison. The baseline case of legacy PW, m₄=4, ishighlighted in bold.

TABLE 1 Bit Distribution in Enhanced PW and FRANK FRANK PW: β = 2^(1/m),K = 64 K = 32 K = 48 K = 64 K = 80 K = 96 m₁ m₂ m₃ m₄ m₅ K00[0, 63] 0 00 0 0 0 0 0 0 0 K01[64, 127] 0 0 0 0 0 0 0 0 0 1 K02[128, 1911 0 0 0 0 00 0 0 0 1 K03[192, 255] 1 2 4 6 9 1 2 5 7 8 K10[256, 319] 0 0 0 0 1 0 00 1 1 K11[320, 383] 2 4 7 10 14 8 10 11 11 10 K2[384, 447] 3 7 11 16 2019 17 16 15 14 K3[448, 511] 26 35 42 48 52 36 35 32 30 29

Embodiments herein analyze PW code construction alongside FRANK codeconstruction to obtain a modified PW code that offers improvedefficiency for polar code construction. The two methods were examined toidentify any substantive differences from an implementation standpoint.Though higher than that of FRANK, PW latency is small (<50 μs) which iscomparable to anticipated slot times for NR. Consequently, the latencyfor code construction may be hidden in the previous block decodingperiod.

Finite precision effects with PW construction are negligible (>75 dBbelow the signal of interest) suggesting online computation is feasibleeven for modest bit-widths. In contrast, finite precision requirementswith FRANK may benefit from careful treatment to ensure that encoder anddecoder arrive at the same K-distribution of bits. This is especiallytrue for long block sizes and low code rates, where R^(M) terms begin todominate.

Given the potential for online computation, the K-distribution forenhanced PW may be tuned to approximate that for FRANK providing asimilar starting point for rate matching considerations, but without thelimitation of the finite precision effects of FRANK.

Enhanced PW: Numerical Example

The following list describes numerical results with β-expansion used inimplementing enhanced PW for the particular case of, N=16, m=4,β_(i)=2^(j·1/4), assuming transmission over additive Gaussian whitenoise (AWGN).

Polarization Weights:

w₀ ¹⁵ [0 1.0000 1.1892 2.1892 1.4142 2.4142 2.6034 3.6034 1.6818 2.68182.8710 3.8710 3.0960 4.0960 4.2852 5.2852].

Ordered Sequence:

q₀ ¹⁵ [0 1 2 4 8 3 5 6 9 10 12 7 11 13 14 15].

The following sample channel assignment results for K=6 assuming anall-zero frozen bit field:

u₀ ¹⁵=[0 0 0 0 0 0 0 0 0 u₀ u₂ 0 u₁ u₃ u₄ u₅].

FIG. 11: Nested β-Expansion, N=512 (n=9), m=4

FIG. 12 is a tree diagram illustrating an ordered sequence of terms inβ-expansion for a particular value of m. Note that the ordered sequencemay be tuned by altering β (i.e., by altering m) to resemble the UPOreliabilities.

The UPO with regard to channel reliabilities is preserved providedcertain conditions are met. For example:

C(W ⁰⁰¹¹)>C(W ¹⁰⁰⁰)β+1>β³; equivalently β³−(β+1)<0

C(W ¹⁰⁰¹)>C(W ⁰¹¹⁰)β³+1>β²+β; equivalently β²+β−(β³+1)<0

For example, FIG. 13 illustrates where one or more of these basicconditions is not met for m<2.5. The chosen value of m may be governedby other considerations including the distribution of information bitsto lower K-positions as well as the resulting error performance.

FIG. 12: β-Expansion, N=512 (n=9), m=4

FIG. 14 illustrates an example ordered sequence of information bitallocation, q₀ ^(N−1), for N=512 and m=4. As illustrated, the computedreliabilities generally increase with synthetic channel index, withstaggered drops as the index increases.

FIG. 13: Recursive Information Bit Allocation

FIG. 15 is a schematic diagram illustrating the recursive bit allocationprocess employed by FRANK. As illustrated, FRANK prescribes a recursiveprocedure for distributing information bits according to the assignedcode rate such that:

The proportion of information bits allocated to the W⁰ channelinstances: K0=R0/R*K/2.

The proportion of information bits allocated to the W¹ channelinstances: K1=R1/R*K/2.

The code rate reflects the average W¹ and W⁰ mutual information,respectively: R=(R0+R1)/2.

Carried out in multiple stages, FRANK aims to break the bit assignmentsinto fixed-length sub-blocks. Using FIG. 15 as an example, lowerK-positions may be defined as those having a lower index in a givenstage. Given this definition, K000 represents a lower K-position ascompared to K001, K000-K001 occupy lower K-positions than do K010-K011,and so on.

The approach may result in information bit distribution to lowerK-positions in proportion to the intended code rate. The higher the coderate, the greater the number of information bits allocated to lowerK-positions. By comparison the PW bit distribution is relatively flat inthe lower K-positions as a function of code rate (See Reference 4). Forexample, see K011[192, 255], highlighted in bold in Table 2 below.

TABLE 2 K distribution of N = 512 PDCCH codes FRANK PW: β = 2¼ K = 32 K= 48 K = 64 K = 80 K = 96 K = 32 K = 48 K = 64 K = 80 K = 96 R = K/N0.0625 0.0938 0.125 0.1563 0.1875 0.0625 0.0938 0.125 0.1563 0.1875K000[0, 63] 0 0 0 0 0 0 0 0 0 0 K001[64, 127] 0 0 0 0 0 0 0 0 0 0K010[128, 191] 0 0 0 0 0 0 0 0 0 0 K011[192, 255] 1 2 4 6 9 0 0 0 0 1K100[256, 319] 0 0 0 0 1 0 0 1 1 4 K101[320, 383] 2 4 7 10 14 0 1 6 7 12K110[384, 447] 3 7 11 16 20 4 8 15 21 27 K111[448, 511] 26 35 42 48 5228 39 42 51 52

According to embodiments described herein, implementing enhanced PW withvariable β=2¹/m may enable the bit ordering to be tuned to deliver aninformation bit distribution using the enhanced PW method whichapproximates that delivered by FRANK. For example, Table 3 illustratesthat the allocation of bits in K011[192, 255] was tuned usingβ-expansion with enhanced PW to match that delivered by FRANK.

TABLE 3 K distribution with variable β enhanced PW: β = 2^(1/m) FRANK K= 32 K = 48 K = 64 K = 80 K = 96 K = 32 K = 48 K = 64 K = 80 K = 96 m =15 m = 14 m = 12 m = 13 m = 12 R = K/N 0.0625 0.0938 0.125 0.1563 0.18750.0625 0.0938 0.125 0.1563 0.1875 K000[0, 63] 0 0 0 0 0 0 0 0 0 0K001[64, 127] 0 0 0 0 0 0 0 0 0 0 K010[128, 191] 0 0 0 0 0 0 0 1 1 2K011[192, 255] 1 2 4 6 9 1 2 4 6 9 K100[256, 319] 0 0 0 0 1 0 0 2 1 2K101[320, 383] 2 4 7 10 14 5 9 15 19 25 KI 10[384, 447] 3 7 11 16 20 0 15 9 13 K111[448, 511] 26 35 42 48 52 26 36 37 44 45

FIG. 14: Permutation in Bit Position

FIG. 16 is a scatterplot diagram of information bit allocation usingenhanced PW for various values of m. The ordered sequence may beinterpreted as the result of a permutation, translating input indices toassigned bit positions. Conventional binary expansion corresponds tom=1, i.e. β=21/1=2, yielding the same ordering as the input sequenceindexing without permutation (the diagonal line in FIG. 16). Thepermutation shows greater variability in information bit allocation withincreasing values of m (i.e. decreasing β). In other words, increasing mabove m=1 leads to information bit allocation that deviates moredramatically from the diagonal line shown in FIG. 16.

Note that with decreasing β (i.e. increasing m), the information bitassignments explore the lower K-positions earlier in bit index, startingfrom index N=512 and proceeding downward. Greater variability in channelpermutation may result in distributing information bits more widely inbit-position, more closely resembling the average mutual information fora given code rate.

FIGS. 15a-17b : Bit Distribution

FIGS. 17a-17b illustrate a side-by-side comparison of the orderedsequence of information bit allocation, q₀ ^(N−1), for N=512 and m=4(left) and m=14 (right). With increasing values of m (i.e. decreasingβ), the resulting channel assignments show considerably more whitespaceat higher channel indices, indicating a more widespread distribution ofinformation bits to the lower K-positions. For example, the larger valueof m=14 is shown to result in considerably more “whitespace”, indicatinga broader (i.e., less monotonic) distribution of information bitallocation. FIGS. 17a-17b illustrate how larger values of m may lead toinformation bit allocation earlier in the polar code, which may moreaccurately approximate the exact channel reliabilities depending on thevalues of the block size and the coding rate.

Channel Ordering

An important measure of code performance is how well the channelassignments conform to the computed channel reliabilities, listed as“IDX” in

FIG. 16 as a function of code rate, R=KIN. The closer the orderedsequence comes in bit index to that indicated by the computed channelreliabilities, the better the code performance.

FIG. 16: Channel Ordering

FIG. 18 illustrates assigned bit position (i.e., information bitallocation) for four different values of the coding rate, K, and forvarious values of m, as compared to the exact channel reliabilities(“IDX”). There is often considerable mismatch in code assignment asβ-expansion (and FRANK for that matter) at best approximates theordering achieved given the computed channel reliabilities. However, forlow code rates, e.g. K=16, it is evident that the error in channelassignment is relatively small independent of the assigned β value. Theerror rates climb steadily with K. However, multiple crossover pointsexist where some values in β yield a closer match on average in assignedbit position than others. For example, the average error in assertedchannel reliability is illustrated in FIG. 19, where the smallest errorindicates the best choice in β for a given block size and code rate.

Table 4, below, lists the values m which yield the smallest averageerror in assigned channel reliability for various values of code rateand various block sizes. In some embodiments, a revised approach may beimplemented whereby m is determined offline based on the value thatminimizes the error in channel reliability for a given code rate andblock size configuration. β-expansion may then continue at run-timetaking β=2^(1/m) as an input parameter for the selected code rate andblock size configuration.

TABLE 4 β-selection based on minimum channel assignment error Code BetaSelection - yielding the smallest error Rate in assigned channelreliability given β = 2^(1/m) 0.0625 3 6 5 3 3 3 3 0.0938 3 6 5 11 3 3 30.1250 5 6 5 11 3 3 3 0.1563 5 6 5 14 6 3 3 0.1875 3 8 7 14 9 3 160.2188 3 8 6 8 9 3 16 0.2500 3 7 5 8 9 3 16 0.2813 12 7 7 8 9 4 160.3125 12 7 7 8 5 4 15 0.3438 12 8 7 8 5 4 15 0.3750 6 8 7 8 5 4 150.4063 6 7 6 8 5 4 15 0.4375 6 7 6 8 3 4 15 0.4688 6 7 6 11 5 3 150.5000 6 7 6 10 3 3 12 0.5313 6 3 6 10 3 3 12 0.5625 6 3 6 10 3 3 120.5938 6 3 6 10 3 3 12 0.6250 6 3 6 10 3 3 12 0.6563 6 3 6 11 3 3 120.6875 6 3 6 11 3 3 12 0.7188 6 3 6 10 3 3 12 0.7500 6 3 6 11 3 3 120.7813 6 3 6 10 3 3 12 0.8125 6 7 6 10 3 3 12 0.8438 6 3 6 10 3 3 120.8750 6 7 6 10 3 3 12 n 6 7 8 9 10 11 12 Block N 64 128 256 512 10242048 4096 Size

Modified β-Expansion Procedure

In some embodiments, a modified β-expansion procedure may be performedas follows:

i. Take B_(i)

b_(n−1)b_(n−2) . . . b₀ as the binary expansion of the synthetic channelindex, i=0, 1, . . . , N−1, where b_(j)∈{0,1}, j [0, 1, . . . , n−1],

ii. Take as input the parameter, m, seen to minimize the error inchannel reliability assignment for a given combination in code rate andblock size.

iii. Compute the polarization weights,

${w_{i} = {\sum\limits_{j = 0}^{n - 1}\; {b_{j} \cdot \beta_{j}}}},{{{where}\mspace{14mu} \beta_{j}} = 2^{j \cdot \frac{1}{m}}}$

-   -   where the integer value, m>0, determines the rate of expansion,

${i.e.w_{i}} = {{b_{n - 1} \cdot 2^{\frac{n - 1}{m}}} + {b_{n - 2} \cdot 2^{\frac{n - 2}{m}}} + \cdots + {b_{0} \cdot {2^{\frac{0}{m}}.}}}$

iv. Sort such that w_(Q) ₀ ≤w_(Q) ₁ ≤w_(Q) ₂ ≤ . . . ≤w_(Q) _(N−1) thensave the resulting indices as the ordered sequence, q₀ ^(N−1).

Inserted in step-ii, the selection of m may involve offline computationto determine a desirable value of m for the assigned code rate/blocksize combination. Online computation may proceed thereafter given theselected value β=2^(1/m). This modified approach may preserve thecomputational efficiency of β-expansion in assessing channelreliabilities at run-time while incorporating a means to tune the bitassignments for a better match in channel reliability as a function ofcode rate.

Embodiments of the present disclosure may be realized in any of variousforms. For example, in some embodiments, the present invention may berealized as a computer-implemented method, a computer-readable memorymedium, or a computer system. In other embodiments, the presentinvention may be realized using one or more custom-designed hardwaredevices such as ASICs. In other embodiments, the present invention maybe realized using one or more programmable hardware elements such asFPGAs.

In some embodiments, a non-transitory computer-readable memory mediummay be configured so that it stores program instructions and/or data,where the program instructions, if executed by a computer system, causethe computer system to perform a method, e.g., any of the methodembodiments described herein, or, any combination of the methodembodiments described herein, or, any subset of any of the methodembodiments described herein, or, any combination of such subsets.

In some embodiments, a computing device may be configured to include aprocessor (or a set of processors) and a memory medium, where the memorymedium stores program instructions, where the processor is configured toread and execute the program instructions from the memory medium, wherethe program instructions are executable to implement any of the variousmethod embodiments described herein (or, any combination of the methodembodiments described herein, or, any subset of any of the methodembodiments described herein, or, any combination of such subsets). Thedevice may be realized in any of various forms.

Although specific embodiments have been described above, theseembodiments are not intended to limit the scope of the presentdisclosure, even where only a single embodiment is described withrespect to a particular feature. Examples of features provided in thedisclosure are intended to be illustrative rather than restrictiveunless stated otherwise. The above description is intended to cover suchalternatives, modifications, and equivalents as would be apparent to aperson skilled in the art having the benefit of this disclosure.

The scope of the present disclosure includes any feature or combinationof features disclosed herein (either explicitly or implicitly), or anygeneralization thereof, whether or not it mitigates any or all of theproblems addressed herein. Accordingly, new claims may be formulatedduring prosecution of this application (or an application claimingpriority thereto) to any such combination of features. In particular,with reference to the appended claims, features from dependent claimsmay be combined with those of the independent claims and features fromrespective independent claims may be combined in any appropriate mannerand not merely in the specific combinations enumerated in the appendedclaims.

1. A method for transmitting polar encoded data, the method comprising:storing data to be polar encoded; determining a reliability associatedwith each bit position in a bit sequence by: calculating a sequence ofweighted summations corresponding to each of the bit positions, whereinthe reliability of each bit position is determined from its respectiveweighted summation, wherein the weighted summation corresponding to eachrespective bit position is a weighted summation over a binary expansionof the respective bit position, wherein the weighted summation isweighted by a first multiplicative factor, and wherein the firstmultiplicative factor is selected based at least in part on a block sizeof the polar code; rank ordering the bit positions based on theirrespective reliabilities; allocating the data as information bits in themost reliable bit positions of a polar code based on the rank ordering;allocating the remaining portion of bit positions as frozen bits of thepolar code; polar encoding the information bits and the frozen bits; andtransmitting the polar encoded information bits and frozen bits.
 2. Themethod of claim 1, the method further comprising: determining a codingrate for polar encoding the data, wherein the multiplicative factor isfurther selected based at least in part on the coding rate.
 3. Themethod of claim 1, wherein the multiplicative factor comprises anexponential quantity comprising a first exponential factor; wherein thefirst exponential factor comprises the exponential power of each term inthe binary expansion being summed over.
 4. The method of claim 3,wherein the exponential quantity further comprises a second exponentialfactor; and wherein the second exponential factor comprises anadjustable parameter that is tunable based at least in part on the blocksize.
 5. The method of claim 4, wherein the second exponential factor isselected to spread a distribution of the information bits of the polarcode to earlier bit positions relative to a distribution obtained fromselecting the second exponential factor to be ¼.
 6. The method of claim1, wherein the multiplicative factor is further selected such that thepolar code approximates a corresponding polar code, wherein thecorresponding polar code implements a coding rate associated with thepolar encoding and is constructed using an alternative polar codeconstruction methodology.
 7. The method of claim 6, wherein the polarcode is constructed according to an enhanced polarization weightingmethodology, and wherein the alternative polar code constructionmethodology comprises a fractally enhanced kernel (FRANK) polar codeconstruction methodology.
 8. The method of claim 1, the method furthercomprising: storing second data to be polar encoded; determining asecond coding rate for polar encoding the second data; determining asecond reliability associated with each bit position in the bitsequence, wherein the second reliability is determined using a secondsequence of weighted summations corresponding to each of the bitpositions, and wherein the weighted summation is weighted by a secondmultiplicative factor that is selected based at least in part on thesecond coding rate, wherein the second multiplicative factor isdifferent from the first multiplicative factor; performing second rankordering of the bit positions based on their respective secondreliabilities; allocating the second data as second information bits inthe most reliable bit positions of a second polar code based on thesecond rank ordering; allocating the remaining portion of bit positionsas second frozen bits of the second polar code; polar encoding thesecond information bits and the second frozen bits; and transmitting thepolar encoded second information bits and second frozen bits.
 9. A userequipment device (UE), comprising: a radio; a memory; and one or moreprocessing elements operably coupled to the radio and the memory;wherein the radio, the memory, and the one or more processing elementsare configured to: store data to be polar encoded in the memory;determine a reliability associated with each bit position in a bitsequence, wherein the reliability of each respective bit position isdetermined by computing a weighted sum over a binary expansion of therespective bit position, and wherein the sum is weighted based at leastin part on a block size of the polar code; rank order the bit positionsbased on their respective reliabilities; and allocate the data asinformation bits in the most reliable bit positions of a polar codebased on the rank ordering; allocate the remaining portion of bitpositions as frozen bits of the polar code; polar encode the informationbits and the frozen bits; and transmit, via the radio, the polar encodedinformation bits and frozen bits.
 10. The UE of claim 9, wherein theradio, the memory, and the one or more processing elements are furtherconfigured to: determine a coding rate associated with the polarencoding, wherein the sum is weighted further based at least in part onthe coding rate.
 11. The UE of claim 9, wherein the weighted summationis weighted by a first multiplicative factor, and wherein weighting thesum based on the block size comprises selecting the first multiplicativefactor based on the block size.
 12. The UE of claim 11, wherein thefirst multiplicative factor comprises an exponential quantity comprisinga first exponential factor; wherein the first exponential factorcomprises the exponential power of each term in the binary expansionbeing summed over.
 13. The UE of claim 12, wherein the exponentialquantity further comprises a second exponential factor; and wherein thesecond exponential factor comprises an adjustable parameter that istunable based at least in part on a coding rate associated with thepolar encoding.
 14. The UE of claim 13, wherein the second exponentialfactor is selected to spread a distribution of the information bits ofthe polar code to earlier bit positions relative to a distributionobtained from selecting the second exponential factor to be ¼.
 15. TheUE of claim 11, wherein the first multiplicative factor is furtherselected such that the polar code approximates a corresponding polarcode, wherein the corresponding polar code implements a coding rateassociated with the polar encoding and is constructed using analternative polar code construction methodology.
 16. The UE of claim 15,wherein the polar code is constructed according to an enhancedpolarization weighting methodology, and wherein the alternative polarcode construction methodology comprises a fractally enhanced kernel(FRANK) polar code construction methodology.
 17. An apparatus,comprising: one or more processing elements configured to cause awireless device to: store data to be polar encoded; determine areliability associated with each bit position in a bit sequence, whereinin determining the reliability associated with each bit position in thebit sequence, the processing element is configured to: calculate asequence of weighted summations corresponding to each of the bitpositions, wherein the reliability of each bit position is determinedfrom its respective weighted summation, wherein the weighted summationcorresponding to each respective bit position is a weighted summationover a binary expansion of the respective bit position, wherein theweighted summation is weighted by a first multiplicative factor, andwherein the first multiplicative factor is selected based at least inpart on a block size of the polar code; rank order the bit positionsbased on their respective reliabilities; allocate the data asinformation bits in the most reliable bit positions of a polar codebased on the rank ordering; allocate the remaining portion of bitpositions as frozen bits of the polar code polar encode the informationbits and the frozen bits; and transmit the polar encoded informationbits and frozen bits.
 18. The apparatus of claim 17, wherein the one ormore processing elements are further configured to cause the wirelessdevice to: determine a coding rate for polar encoding the data, whereinthe multiplicative factor is further selected based at least in part onthe coding rate.
 19. The apparatus of claim 18, wherein themultiplicative factor comprises an exponential quantity comprising afirst exponential factor and a second exponential factor; wherein thefirst exponential factor comprises the exponential power of each term inthe binary expansion being summed over, and wherein the secondexponential factor comprises an adjustable parameter that is tunablebased at least in part on the coding rate.
 20. The apparatus of claim19, wherein the second exponential factor is further selected to spreada distribution of the information bits of the polar code to earlier bitpositions relative to a distribution obtained from selecting the secondexponential factor to be ¼.